DocumentCode :
250085
Title :
AdaPT: Real-time adaptive pedestrian tracking for crowded scenes
Author :
Bera, Aniket ; Galoppo, Nico ; Sharlet, Dillon ; Lake, Adam ; Manocha, Dinesh
Author_Institution :
Dept. of Comput. Sci., Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA
fYear :
2014
fDate :
May 31 2014-June 7 2014
Firstpage :
1801
Lastpage :
1808
Abstract :
We present a novel realtime algorithm to compute the trajectory of each pedestrian in a crowded scene. Our formulation is based on an adaptive scheme that uses a combination of deterministic and probabilistic trackers to achieve high accuracy and efficiency simultaneously. Furthermore, we integrate it with a multi-agent motion model and local interaction scheme to accurately compute the trajectory of each pedestrian. We highlight the performance and benefits of our algorithm on well-known datasets with tens of pedestrians.
Keywords :
multi-agent systems; object tracking; pedestrians; probability; real-time systems; AdaPT; adaptive scheme; crowded scenes; deterministic tracker; local interaction scheme; multiagent motion model; probabilistic tracker; real-time adaptive pedestrian tracking; realtime algorithm; Accuracy; Adaptation models; Computational modeling; Histograms; Target tracking; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location :
Hong Kong
Type :
conf
DOI :
10.1109/ICRA.2014.6907095
Filename :
6907095
Link To Document :
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